MétaCan
Menu
← Back to cohort
Record W1631748814

La Gaspésie : terreau fertile de la chanson au Québec

2015· article· fr· W1631748814 on OpenAlexaboutno aff
Jean-Marie Fallu

Bibliographic record

VenueÉrudit (Université de Montréal) · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

par Jean-Marie Fallu, historien et rédacteur en chef du Magazine Gaspésie La chanson a toujours fait partie du mode de vie des Gaspésiens, à commencer par les premiers d'entre eux, les Mi'gmaq.De passage à Listuguj en 1812, l'évêque de Québec, M gr Plessis, est tellement impressionné par l'art vocal des Mi'gmaq qu'il trouve « leur chant, préférable du côté des voix, à celui de la plupart des villages du Canada 2 .» Pour sa part, le postillon Timothée Auclair, qui livre la « malle » de Sainte-Annedes-Monts à Rivière-au-Renard entre 1856 et 1860, remarque les belles voix féminines qu'il y a dans ce secteur.«Presquetouteslesfi llesavaientde belles voix et chantaient très bien 3 .» La chanson traditionnelle La riche tradition orale gaspésienne attire un lot de folkloristes.Principalement en 1918, 1922 et 1923, Marius Barbeau sauve de l'oubli un patrimoine chanté impressionnant.Il capte sur phonographe et par sténo graphie la mémoire chantée de plusieurs Gaspésiens.« Il apparaissait, note une Gaspésienne, dès l'aurore du jour, sur sa bicyclette, une chanson sur les lèvres.C'était beau de le voir.Ça nous donnait un autre goût de chanter... Mais plus curieux encore, c'était qu'il n'oubliait pas nos chansons.Chaque fois qu'il revenait en Gaspésie, je l'entendais venir de loin 4 !» Publicité du spectacle de Georges Dor au Centre d'art de Percé.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.429

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0150.003
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0260.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.178
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractno

Explore more

Same venueÉrudit (Université de Montréal)→Same topicCanadian Identity and History→French-language works237,207→